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Duration 35 hours
Course Outline
Introduction to AI in Python
- Core concepts and the scope of AI
- Python libraries used for AI development
- Structuring AI projects and defining workflows
Preparing Data for AI
- Data cleaning, transformation, and feature engineering
- Managing missing and unbalanced data
- Feature scaling and encoding techniques
Supervised Learning Methods
- Regression and classification algorithms
- Ensemble methods: Random Forest, Gradient Boosting
- Hyperparameter tuning and cross-validation
Unsupervised Learning Methods
- Clustering techniques: K-Means, DBSCAN, hierarchical clustering
- Dimensionality reduction: PCA, t-SNE
- Practical use cases for unsupervised learning
Neural Networks and Deep Learning
- Introduction to TensorFlow and Keras
- Building and training feedforward neural networks
- Strategies for optimizing neural network performance
Reinforcement Learning (Introduction)
- Core concepts: agents, environments, and rewards
- Implementing basic reinforcement learning algorithms
- Real-world applications of reinforcement learning
Deploying AI Models
- Saving and loading trained models
- Integrating models into applications via APIs
- Monitoring and maintaining AI systems in production environments
Summary and Future Steps
Requirements
- A solid grasp of fundamental Python programming concepts
- Experience working with data analysis libraries such as NumPy and pandas
- Basic knowledge of machine learning concepts and algorithms
Target Audience
- Software developers aiming to broaden their AI development capabilities
- Data analysts seeking to apply AI techniques to complex datasets
- R&D professionals building AI-powered applications
Testimonials (2)
The trainer was very available to answer all te kind of question I did
Caterina - Stamtech
Course - Developing APIs with Python and FastAPI
Trainer develops training based on participant's pace
